Desok Kim

Ultra Short Term Analysis of Heart Rate Variability for Monitoring Mental Stress in Mobile Settings

Heart rate variability (HRV) analysis is commonly used as a quantitative marker depicting the activity of autonomous nervous system (ANS) that may be related to mental stress. For mobile applications, short term ECG measurement may be used for HRV analysis since the conventional five minute long recordings might be inadequately long. Short term analysis of HRV features has been investigated mostly in ECG data from normal and cardiac patients. Thus, short term HRV features may not have any relevance on the assessment of acute mental stress. In this study, we obtained ultra short term HRV features from 24 subjects during baseline stage and Stroop color word test. We validated these HRV features by showing significant differences in HRV features existed between the two stages. Our results indicated that ultra short term analysis of heart rate and RR intervals within 10 s, RMSSD and PNN50 within 30 s, HF within 40 s, LF/HF, normalized LF, and normalized HF within 50 s could be reliably performed for monitoring mental stress in mobile settings.

Detection of Acute Stress by Heart Rate Variability Using a Prototype Mobile ECG Sensor

Mental stress affects our body often detrimentally. Heart rate variability (HRV) analysis is commonly used as a quantitative marker depicting the activity of autonomic nervous system that may be related to mental stress. HRV features can be extracted by detecting QRS complexes from electrocardiogram (ECG) signals. A miniature ECG device can enable HRV features to be measured anytime and anywhere. We developed an ECG analysis software suitable for signals generated from a prototype mobile ECG sensor. Our analysis software was able to detect 99.3% of the QRS complexes correctly. HRV features were different by less than 3.0% when a well-validated heartbeat sensor was simultaneously used. Significant changes of HRV features were detected during Stroop color word test that induced acute stress. We showed a feasibility of using a mobile ECG sensor to detect subtle changes in HRV features that may enable stress monitoring and management in daily life.

Ultra Short Term Analysis of Heart Rate Variability using Normal Sinus Rhythm and Atrial Fibrillation ECG Data

Heart rate variability (HRV) analysis is well established as a quantitative predictor of clinical cardiac events. To provide reliable assessment of HRV in mobile settings, short duration ECG recordings may be analyzed since the conventional five min measurement might be inadequately long. In this study, HRV features were calculated in variable time lengths of long term mobile ECG data. The ultra short term HRV features as reliable as five min measurement were found using Kruskal-Wallis test (p>0.05). However, these HRV features may not have any clinical relevance. Thus, two sets of HRV features were calculated from varying lengths of normal sinus rhythm and atrial fibrillation ECG data. Finally, ultra short term HRV features were obtained from the shortest ECG data segments that produced significant differences between the two sets of HRV features. These results suggested that we could assess the cardiac activity of individuals accurately and conveniently by ultra short term ECG recordings from mobile sensors.

Dependence of Heart Rate Variability on Stress Factors of Stress Response Inventory

Heart rate variability (HRV) analysis is commonly used as a quantitative marker depicting the activity of autonomic nervous system (ANS) related to mental stress. Stress response inventory (SRI) has been devised to score mental and physical symptoms occurred during the past two weeks. SRI is composed of seven stress factors that may influence the status of mental stress levels. In this study, we investigated the relationships of physiological measures and HRV features on ages and stress factors. Physiological measures and HRV features in low (SRI scores: 7.1 plusmn 4.1, n=225) and high stress group (22.5 plusmn 7.4, n=135) were compared with age as the covariate (ANCOVA). Age was reconfirmed as a significant factor influencing physiological measures and most of HRV features. Age was also inversely correlated to stress factor scores. Systolic blood pressure, glucose level, and normalized HF were significantly lower, whereas body temperature, LF/HF, and normalized LF were significantly higher in high stress group. Our results showed that stress levels were associated with ages, physiological measures, and HRV features.

Detection of subjects with higher self-reporting stress scores using heart rate variability patterns during the day

Heart rate variability (HRV) has been well established to measure instantaneous levels of mental stress. Circadian patterns of HRV features have been reported but their use to estimate levels of mental stress were not studied thoroughly. In this study, we investigated time dependent variations of HRV features to detect subjects under chronic mental stress. Sixty eight subjects were divided into high (n=10) and low stress group (n=43) depending on their self-reporting stress scores. HRV features were calculated during three different time periods of the day. High stress group showed decreased patterns of HRV features compared to low stress group. When logistic regression analysis was performed with raw multiple HRV features, the classification was 63.2% accurate. A new % deviance score reflecting the degree of difference from normal reference patterns increased the accuracy to 66.1%. Our data suggested that HRV patterns obtained at multiple time points of the day could provide useful data to monitor subjects under chronic stress.

Detection of atrial fibrillation episodes using multiple heart rate variability features in different time periods

Circadian variations of cardiac diseases have been well known. For example, atrial fibrillation (AF) episodes show nocturnal predominance. In this study, we have developed multiple formulas that detect AF episodes in different times of the day. Heart rate variability features were calculated from randomly sampled three min ECG data. Logistic regression analyses were performed to generate three formulas for the entire day, daytime, and evening time. Compared to the first formula that disregarded the time of the day, the second formula for the daytime detection detected AF episodes more accurately (95.2% vs. 99.3%), whereas third formula for the evening time detection did less accurately (93.8%). These results suggest the detection of AF episodes might become more accurate by considering the time-dependent changes of HRV features. In addition, the detection method for the evening time requires further investigation.

Java Web Start based software for automated quantitative nuclear analysis of prostate cancer and benign prostate hyperplasia.

BackgroundAndrogen acts via androgen receptor (AR) and accurate measurement of the levels of AR protein expression is critical for prostate research. The expression of AR in paired specimens of benign prostate and prostate cancer from 20 African and 20 Caucasian Americans was compared to demonstrate an application of this system.MethodsA set of 200 immunopositive and 200 immunonegative nuclei were collected from the images using a macro developed in Image Pro Plus. Linear Discriminant and Logistic Regression analyses were performed on the data to generate classification coefficients. Classification coefficients render the automated image analysis software independent of the type of immunostaining or image acquisition system used. The image analysis software performs local segmentation and uses nuclear shape and size to detect prostatic epithelial nuclei. AR expression is described by (a) percentage of immunopositive nuclei; (b) percentage of immunopositive nuclear area; and (c) intensity of AR expression among immunopositive nuclei or areas.ResultsThe percent positive nuclei and percent nuclear area were similar by race in both benign prostate hyperplasia and prostate cancer. In prostate cancer epithelial nuclei, African Americans exhibited 38% higher levels of AR immunostaining than Caucasian Americans (two sided Students t-tests; P < 0.05). Intensity of AR immunostaining was similar between races in benign prostate.ConclusionThe differences measured in the intensity of AR expression in prostate cancer were consistent with previous studies. Classification coefficients are required due to non-standardized immunostaining and image collection methods across medical institutions and research laboratories and helps customize the software for the specimen under study. The availability of a free, automated system creates new opportunities for testing, evaluation and use of this image analysis system by many research groups who study nuclear protein expression.

Short Term Analysis of Long Term Patterns of Heart Rate Variability in Subjects under Mental Stress

Long term patterns of heart rate variability (HRV) features were decreased in subjects with higher self reporting stress scores. For mobile applications, short term analysis of HRV features may be ideal since conventional heartbeat recordings (3-5 min) might be inadequately long. In this study, short term analysis has been performed for heartbeat data obtained at five different time points from two subject groups (15 under high and 18 under low mental stress). The reliability of short term heartbeat data was demonstrated by detecting significant differences in long term patterns of HR V features between two groups. Fifteen to thirty second heartbeat measurements were long enough to produce reliable long term patterns of HRVfeatures. Thus, short and intermittent recordings of heartbeats could be used to detect long term HR Vpatterns and offer a convenient method to monitor mental stress in mobile environments.

Detection of Long Term Variations of Heart Rate Variability in Normal Sinus Rhythm and Atrial Fibrillation ECG Data

Circadian variations of heart rate variability (HRV) have been well known in cardiac diseases. However, long term HRV features were not thoroughly investigated for the prediction of atrial fibrillation (AF). Thus, we analyzed the 15 hour long changes of HRV of normal sinus rhythm (NSR) and AF data. Long term patterns of HR V in NSR were established first and normal data of AF were shown different to NSR. Due to long term changes of HRV patterns, two formulas were provided higher accuracy (93%) than single formula (79%) in detecting normal data of AF. Furthermore, HRV features representing 5 and 30 min before the AF onset showed significant temporal changes and the dynamics of these changes were also different depending on the recording periods. These data suggest that the onset of AF could be predicted more accurately by considering the long term temporal variations of HRV features.

Three Dimensional Volume Measurement of Mouse Abdominal Fat in Magnetic Resonance Images

Obesity adversely affects the health of people. Therapeutics against obesity could be validated by measuring the change of body fat tissues repeatedly in magnetic resonance (MR) images. In this study, an accurate method for three dimensional (3D) volume measurement of abdominal fat tissue has been developed for mouse MR images. The MR image was acquired by gradient echo technique and preprocessed by low pass filtering. 3D images were segmented by three-level adaptive thresholding based on the intensity histogram. Small objects were removed by erosion followed by binary reconstruction. Fat tissues were separated by ultimate erosion and individual labeling in 3D, followed by conditional dilation. Abdominal subcutaneous and visceral fat tissues were interactively classified and compared to manually obtained ground truth images. Measurement accuracy was greater than 83.4% for total body fat and 81.9% for abdominal visceral fat, showing the feasibility of routinely measuring the specific component of body fats in mice.